This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. The subproject and investigator (PI) may have received primary funding from another NIH source, and thus could be represented in other CRISP entries. The institution listed is for the Center, which is not necessarily the institution for the investigator. Cochlear implants are used in profoundly deaf individuals to partially restore the sense of hearing. These implants electrically stimulate the cochlea in response to auditory stimuli measured at the implant's microphone. To date, the algorithms used to convert the auditory stimuli to the pattern of electrical stimuli applied over the length of the cochlea have been quite simple and have not been optimized for word recognition. We are interested in developing parametric stimulation algorithms and optimizing the parameters to minimize the perceptual difference between the neural activation patterns (neural firing probability over time and position in the cochlea) of normal hearing listeners and individuals using cochlear implants. For reasons of practicality, it is necessary to use a simulator to compute the neural activation patterns. This simulator is a Monte Carlo simulation, making it well suited to parallel computing. However, given the iterative nature of the optimizations we wish to perform, many such simulations must be made. Thus, access to a computer cluster would be quite advantageous.